Search

Brain cognitive functions arise through the coordinated activity of several brain regions, which actually form complex dynamical systems operating at multiple frequencies. These systems often consist of interacting subsystems, whose characterization is of importance for a complete understanding of the brain interaction processes. To address this issue, we present a technique, namely the bispectral Pairwise Interacting Source Analysis (biPISA), for analyzing systems of cross-frequency interacting brain sources when multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data are available. Specifically, the biPISA allows to identify one or many subsystems of cross-frequency interacting sources by decomposing the antisymmetric components of the cross-bispectra between EEG or MEG signals, based on the assumption that interactions are pairwise. Thanks to the properties of the antisymmetric components of the cross-bispectra, biPISA is also robust to spurious interactions arising from mixing artifacts, i.e. volume conduction or field spread, which always affect EEG or MEG functional connectivity estimates. This method is an extension of the Pairwise Interacting Source Analysis (PISA), which was originally introduced for investigating interactions at the same frequency, to the study of cross-frequency interactions. The effectiveness of this approach is demonstrated in simulations for up to three interacting source pairs, and for real MEG recordings of spontaneous brain activity. Simulations show that the performances of biPISA in estimating the phase difference between the interacting sources are affected by the increasing level of noise rather than by the number of the interacting subsystems. The analysis of real MEG data reveals an interaction between two pairs of sources of central mu and beta rhythms, localizing in the proximity of the left and right central sulci., Comment: Added funding acknowledgements to Metadata. Acknowledgements: "This project has received funding from the European Commission Horizon 2020 research and innovation program under Grant Agreement No. 686865 (BREAKBEN - H2020-FETOPEN-2014-2015/H2020-FETOPEN-2014-2015-RIA). The content reflects only the author's view and the European Commission is not responsible for the content."

Several scalp EEG functional connectivity studies, mostly clinical, seem to overlook the reference electrode impact. The subsequent interpretation of brain connectivity is thus often biased by the choice a non-neutral reference. This study aims at systematically investigating these effects. As EEG reference, we examined: the vertex electrode (Cz), the digitally linked mastoids (DLM), the average reference (AVE), and the Reference Electrode Standardization Technique (REST). As a connectivity metric, we used the imaginary part of coherency. We tested simulated and real data (eyes open resting state), by evaluating the influence of electrode density, effect of head model accuracy in the REST transformation, and impact on the characterization of the topology of functional networks from graph analysis. Simulations demonstrated that REST significantly reduced the distortion of connectivity patterns when compared to AVE, Cz and DLM references. Moreover, the availability of high-density EEG systems and an accurate knowledge of the head model are crucial elements to improve REST performance. For real data, a systematic change of the spatial pattern of functional connectivity depending on the chosen reference was also observed. The distortion of connectivity patterns was larger for the Cz reference, and progressively decreases when using the DLM, the AVE, the REST. Strikingly, we also showed that network attributes derived from graph analysis, i.e., node degree and local efficiency, are significantly influenced by the EEG reference choice. Overall, this study highlights that significant differences arise in scalp EEG functional connectivity and graph network properties, in dependence of the chosen reference. We hope our study will convey the message that caution should be taken when interpreting and comparing results obtained from different laboratories when using different reference schemes., Comment: Added funding acknowledgements to Metadata. Acknowledgements: "This project has received funding from the European Commission Horizon 2020 research and innovation program under Grant Agreement No. 686865 (BREAKBEN - H2020-FETOPEN-2014-2015/H2020-FETOPEN-2014-2015-RIA). The content reflects only the author's view and the European Commission is not responsible for the content."